Abstract
Copy-paste forgery, which is widely used in the field of audio forgery, is created by copying an audio part in the audio and pasting it in a different location in the same audio. While this type of forgery is quite easy to implement thanks to advanced audio software, post-processing operations applied to forged audio by attackers to hide traces of forgery make this forgery detection extremely difficult. For this purpose, a new post-processing-robust method for detecting audio copy-paste forgery using a key point-based approach on the high-resolution spectrogram image obtained from the audio is proposed. In the proposed method, firstly, a high-resolution spectrogram image is obtained from the audio file. Then, with the Akaze method, key points, and feature descriptors are extracted from the spectrogram image. Extracted features are matched with the g2NN algorithm. Audio copy-paste forgery is detected by tracing the key points on the spectrogram onto the audio. The results obtained show that the proposed method detects audio copy-paste forgery with very high accuracy when compared to the studies in the literature, even if post-processing operations are applied.
Keywords
Project Number
122E013
References
- Alcantarilla, P. F., Bartoli, A., & Davison, A. J. (2012). KAZE features. In Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI 12 (pp. 214-227). Springer Berlin Heidelberg.
- Alcantarilla, P. F., Nuevo, J., & Bartoli, A. (2013). Fast explicit diffusion for accelerated features in nonlinear scale spaces british machine vision conference (BMVC).
- Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE transactions on information forensics and security, 6(3), 1099-1110 DOI:10.1109/TIFS.2011.2129512
- BURUCU, E. (2023). Adli Bilimlerde Ses Kayıtları Üzerinde Manipülasyon İncelemesi. Hacettepe Üniversitesi Edebiyat Fakültesi Dergisi, 40(1) DOI:10.32600/huefd.1106795
- Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395 DOI:10.1145/358669.358692
- Huang, X., Liu, Z., Lu, W., Liu, H., & Xiang, S. (2020). Fast and effective copy-move detection of digital audio based on auto segment. In Digital forensics and forensic Investigations: Breakthroughs in Research and Practice (pp. 127-142). IGI Global DOI:10.4018/IJDCF.2019040104
- Imran, M., Ali, Z., Bakhsh, S. T., & Akram, S. (2017). Blind detection of copy-move forgery in digital audio forensics. IEEE Access, 5, 12843-12855 DOI:10.1109/ACCESS.2017.2717842
- Nam, J., Mysore, G. J., Ganseman, J., Lee, K., & Abel, J. S. (2010). A super-resolution spectrogram using coupled PLCA. In Eleventh Annual Conference of the International Speech Communication Association.
- Ustubioglu, B., Küçükuğurlu, B., & Ulutas, G. (2022). Robust copy-move detection in digital audio forensics based on pitch and modified discrete cosine transform. Multimedia Tools and Applications, 81(19), 27149-27185 DOI:10.1007/s11042-022-13035-3.
- Ustubioglu, B., Tahaoglu, G., & Ulutas, G. (2023). Detection of audio copy-move-forgery with novel feature matching on Mel spectrogram. Expert Systems with Applications, 213, 118963 DOI:10.1016/j.eswa.2022.118963.
- Wang, F., Li, C., & Tian, L. (2017, October). An algorithm of detecting audio copy-move forgery based on DCT and SVD. In 2017 IEEE 17th International Conference on Communication Technology (ICCT) (pp. 1652-1657). IEEE.
- Xiao, J. N., Jia, Y. Z., Fu, E. D., Huang, Z., Li, Y., & Shi, S. P. (2014). Audio authenticity: Duplicated audio segment detection in waveform audio file. Journal of Shanghai Jiaotong University (Science), 19, 392-397 DOI:10.1007/s12204-014-1515-5.
- Xie, Z., Lu, W., Liu, X., Xue, Y., & Yeung, Y. (2018). Copy-move detection of digital audio based on multi-feature decision. Journal of information security and applications, 43, 37-46 DOI:10.1016/j.jisa.2018.10.003
- Yan, Q., Yang, R., & Huang, J. (2015, April). Copy-move detection of audio recording with pitch similarity. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1782-1786). IEEE.
- Yan, Q., Yang, R., & Huang, J. (2019). Robust copy–move detection of speech recording using similarities of pitch and formant. IEEE Transactions on Information Forensics and Security, 14(9), 2331-2341 DOI:10.1109/TIFS.2019.2895965
Abstract
Ses sahteciliği alanında yaygın olarak kullanılan Kopyala-yapıştır sahteciliği, ses içerisindeki bir sesli kısmın kopyalanıp yine aynı ses içerisinde farklı bir konuma yapıştırılmasıyla oluşturulmaktadır. Gelişmiş ses yazılımları sayesinde bu tür bir sahteciliğin uygulanması oldukça kolay olmakla birlikte, saldırganlar tarafından sahtecilik izlerini gizlemek için sahte sese uygulanan son işlem operasyonları bu sahtecilik tespitini oldukça zor hale getirmektedir. Bu amaçla, sesten elde edilen yüksek çözünürlüklü spektrogram görüntüsü üzerinde anahtar nokta tabanlı bir yaklaşım kullanarak, ses kopyala-yapıştır sahteciliğini tespit eden son işlem operasyonlarına dayanıklı yeni bir yöntem önerilmiştir. Önerilen yöntemde öncelikle ses dosyasından yüksek çözünürlüklü spektrogram görüntüsü elde edilir. Ardından, Akaze yöntemi ile spektrogram görüntüsünden anahtar noktalar ve özellik tanımlayıcıları çıkarılmaktadır. Çıkartılan özellikler g2NN algoritması ile eşleştirilmektedir. Spektrogram üzerindeki noktaların ses üzerine iz düşürülmesiyle de ses kopyala-yapıştır sahteciliği tespit edilmektedir. Elde edilen sonuçlar önerilen yöntemin son işlem operasyonları uygulansa dahi literatürdeki çalışmalarla kıyaslandığında çok yüksek doğrulukla ses kopyala-yapıştır sahteciliği tespitini yaptığını göstermektedir.
Keywords
Supporting Institution
TÜBİTAK
Project Number
122E013
References
- Alcantarilla, P. F., Bartoli, A., & Davison, A. J. (2012). KAZE features. In Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI 12 (pp. 214-227). Springer Berlin Heidelberg.
- Alcantarilla, P. F., Nuevo, J., & Bartoli, A. (2013). Fast explicit diffusion for accelerated features in nonlinear scale spaces british machine vision conference (BMVC).
- Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE transactions on information forensics and security, 6(3), 1099-1110 DOI:10.1109/TIFS.2011.2129512
- BURUCU, E. (2023). Adli Bilimlerde Ses Kayıtları Üzerinde Manipülasyon İncelemesi. Hacettepe Üniversitesi Edebiyat Fakültesi Dergisi, 40(1) DOI:10.32600/huefd.1106795
- Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395 DOI:10.1145/358669.358692
- Huang, X., Liu, Z., Lu, W., Liu, H., & Xiang, S. (2020). Fast and effective copy-move detection of digital audio based on auto segment. In Digital forensics and forensic Investigations: Breakthroughs in Research and Practice (pp. 127-142). IGI Global DOI:10.4018/IJDCF.2019040104
- Imran, M., Ali, Z., Bakhsh, S. T., & Akram, S. (2017). Blind detection of copy-move forgery in digital audio forensics. IEEE Access, 5, 12843-12855 DOI:10.1109/ACCESS.2017.2717842
- Nam, J., Mysore, G. J., Ganseman, J., Lee, K., & Abel, J. S. (2010). A super-resolution spectrogram using coupled PLCA. In Eleventh Annual Conference of the International Speech Communication Association.
- Ustubioglu, B., Küçükuğurlu, B., & Ulutas, G. (2022). Robust copy-move detection in digital audio forensics based on pitch and modified discrete cosine transform. Multimedia Tools and Applications, 81(19), 27149-27185 DOI:10.1007/s11042-022-13035-3.
- Ustubioglu, B., Tahaoglu, G., & Ulutas, G. (2023). Detection of audio copy-move-forgery with novel feature matching on Mel spectrogram. Expert Systems with Applications, 213, 118963 DOI:10.1016/j.eswa.2022.118963.
- Wang, F., Li, C., & Tian, L. (2017, October). An algorithm of detecting audio copy-move forgery based on DCT and SVD. In 2017 IEEE 17th International Conference on Communication Technology (ICCT) (pp. 1652-1657). IEEE.
- Xiao, J. N., Jia, Y. Z., Fu, E. D., Huang, Z., Li, Y., & Shi, S. P. (2014). Audio authenticity: Duplicated audio segment detection in waveform audio file. Journal of Shanghai Jiaotong University (Science), 19, 392-397 DOI:10.1007/s12204-014-1515-5.
- Xie, Z., Lu, W., Liu, X., Xue, Y., & Yeung, Y. (2018). Copy-move detection of digital audio based on multi-feature decision. Journal of information security and applications, 43, 37-46 DOI:10.1016/j.jisa.2018.10.003
- Yan, Q., Yang, R., & Huang, J. (2015, April). Copy-move detection of audio recording with pitch similarity. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1782-1786). IEEE.
- Yan, Q., Yang, R., & Huang, J. (2019). Robust copy–move detection of speech recording using similarities of pitch and formant. IEEE Transactions on Information Forensics and Security, 14(9), 2331-2341 DOI:10.1109/TIFS.2019.2895965
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Forensics |
| Journal Section | Computer Engineering |
| Authors | |
| Project Number | 122E013 |
| Publication Date | December 3, 2023 |
| Submission Date | July 23, 2023 |
| Published in Issue | Year 2023 |